Predictive Analysis – a 3rd Generation BI approach - to enhance business operation and Marketing Efforts
A secondary research paper on Predictive analytics; which is a mix of tools and techniques that support organizations to identify probability in data that can be used find out the future outcomes. The scope this study is to identify the potential of predictive analytics to leverage advertising, marketing campaign and business development initiatives thereby understanding the customer behavior, customer preferences, change, attitudes, purchase behaviors and attaining a high degree of confidence in their decisions about what to do differently for each segment, as potential moves have been “pre-tested.”
ffective Marketing Activities + Higher Conversions = More Revenue = Growth & Success! In a tough competitive global marketplace, to have desired return on the marketing initiatives B2b organizations are looking forward to have new avenues which could help them to make a better understand about their customer preferences, change, attitudes, purchase behaviors. Earlier the research was archeological, looking at past customer choices and behavior. With the advent of a third-generation approach called predictive segmentation; B2B markets are able to resolve the challenges and take a competitive advantage. It is a mix of tools and techniques that support organizations to identify probability in data that can be used find out the future outcomes. It helps to tune insights about exactly which elements of the service or product offer actually drive customer behavior and thereby giving a high degree of confidence in their decisions about what to do differently for each segment, because potential moves have been “pre-tested.”
Predictive analytics technology incorporates data collection, statistics, modeling and deployment capabilities, and drives the entire segmentation process, from gathering customer information at every interaction to analyzing the data and providing specific, real-time recommendations on the best action to take at a particular time, with a particular customer. The result is more effective customer relationship management strategies, including advertising and marketing campaigns; upsell and cross-sell initiatives; and long-term customer loyalty, retention and rewards programs.
Current market situation
Most B2B companies which tries to get deeper customer understanding and move segmentation beyond traditional way using selects from industry, size, andgeographic views of customers is not reaching up to the standard. In a recent study by the Institute for theStudy of Business Markets, which surveyed the top business marketers in the United States, themost pressing concern identified by respondents was “finding a better way to expand understandingof their customer needs, market segments, and the key drivers of customer value.” Companies which have traditionally relied on technological innovation to attain competitive advantage have come to realize that new technology or new product features are not good enough to attract more customers or increase revenues from existing customers. Major challenges
1. Sales cycles are long and complex offerings.
2. Competitor’s offerings and strategies shift so quickly that managers cannot reliably compare the impact of changes in a given marketing 3. Customer relationship management systems cannot easily capture the decisions and actions that led to success or failure with any particular account, because such information is largely anecdotal, not quantitative.
The following table represents some examples of the types of challenges solved by predictive marketing for different types of digital marketers:
Benefits or Strategic objectives Attained through Predictive Analysis The predictive approach not only produces forward-looking segments; it also gives...
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